20 citations · 122 across the 25 of their papers we have counts for
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astro-ph.IM2023★ 2 cited
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
D. Huppenkothen, M. Ntampaka, M. Ho +19
Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of…
astro-ph.IM2023★ 18 cited
Astronomaly at scale: searching for anomalies amongst 4 million galaxies
Verlon Etsebeth, Michelle Lochner, Mike Walmsley +1
Modern astronomical surveys are producing datasets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with…